Small unmanned aerial systems detection and classification using multi-modal deep neural networks

ABSTRACT

Provided is a detection and classification system and method for small unmanned aircraft systems (sUAS). The system and method detect and classify multiple simultaneous heterogeneous RC transmitters/sUAS downlinks from the RF signature using Object Detection Deep Convolutional Neural Networks (DCNNs). The method further utilizes not only passive RF, but may also utilize Electro Optic/Infrared (EO/IR), radar and acoustic sensors as well, with a fusion of the individual sensor classifications. Detection and classification with Identification Friend or Foe (IFF) of individual sUAS in a swarm, multi-modal approach for high confidence classification, decision, and implementation on a low C-SWaP (cost, size, weight and power) NVIDIA Jetson TX2 embedded AI platform is achieved.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional PatentApplication Ser. No. 63/230,927, filed Aug. 9, 2021, and entitled “SUASDETECTION AND CLASSIFICATION USING MULTI-MODAL DEEP NEURAL NETWORKS,”the disclosure of which is expressly incorporated by reference herein.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

The invention described herein was made in the performance of officialduties by employees of the Department of the Navy and may bemanufactured, used and licensed by or for the United States Governmentfor any governmental purpose without payment of any royalties thereon.This invention (Navy Case 2005130502) is assigned to the United StatesGovernment and is available for licensing for commercial purposes.Licensing and technical inquiries may be directed to the TechnologyTransfer Office, Naval Surface Warfare Center Crane, email:Cran_CTO@navy.mil.

FIELD OF THE INVENTION

The field of invention relates generally to detection of unmanned aerialsystems and, more particularly, to systems and methods for detection andclassification of small unmanned aerial systems using multi-modal neuralnetworks.

BACKGROUND

Unmanned Aircraft Systems (UAS) have become ubiquitous in recent years.There are five classes of UAS as defined by the FAA and Department ofDefense. The most common class for non-military purposes is Group 1,which typically includes UASs that are less than 20 pounds in weight,and normally operate below 1200 feet above ground level and at speedsless than 250 knots. These UASs are often referred to as Small UnmannedAircraft Systems (sUAS). The proliferation of sUASs has resulted in anurgent need for detection and classification of such aircraft systemsincluding detection that provides, among other things, the radio control(RC) transmitter and vehicle type information to enable monitoring ofthese craft. Furthermore, known reactive Counter-Unmanned AircraftSystems (C-UAS) currently perform scan and jam functions with noknowledge of the threat emitter beyond recorded frequencies.Accordingly, for C-UAS there is a need for identifying vehicle/RCtransmitter type to the Electronic Warfare/Kinetic/Directed Energyweapon operator before any negation action is taken especially in urbanenvironments with non-threat emitters.

SUMMARY

The present invention provides systems and methods for detection andclassification of Small Unmanned Aircraft Systems (sUAS). The inventivesystems and methods detect and classify multiple simultaneousheterogeneous RC transmitters/sUAS downlinks from the RF signature usingObject Detection Deep Convolutional Neural Networks (DCNNs). The RFdetection in some embodiments may be passive RF detection. In additionto passive RF detection, Electro Optic/Infrared (EO/IR), Radar andAcoustic sensors may also be utilized with a Softmax score fusionalgorithm or similar method to effect the individual sensorclassifications. Detection and classification may yet further includeIdentification Friend or Foe (IFF) of individual sUAS in a swarm,multi-modal approach for high confidence classification, decision, andimplementation with a low C-SWaP (cost, size, weight and power)platform.

Additional features and advantages of the present invention will becomeapparent to those skilled in the art upon consideration of the followingdetailed description of the illustrative embodiment exemplifying thebest mode of carrying out the invention as presently perceived.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description of the drawings particularly refers to theaccompanying figures in which:

FIG. 1 shows an example of a multi-modal sUAS detection andclassification system according to aspects of the present disclosure.

FIG. 2 shows an example of a passive RF sensor that may be used with thesystem of FIG. 1 according to aspects of the present disclosure.

FIG. 3 shows an example illustration of a simultaneous detection andclassification of heterogeneous transmitter frequency hops, Lightbridgebursts and background using a deep convolutional neural network (DCNN)according to aspects of the present disclosure.

FIG. 4 shows a block diagram of an exemplary apparatus that may beutilized for RF detection and classification according to aspects of thepresent disclosure.

FIG. 5 illustrates an example of a frequency hop sequence implemented bya radio control (RC) handheld controller for a particular sUAS.

FIG. 6 shows an example of a frequency hop sequence input to trainedlong short-term memory (LSTM) Recurrent Neural Network and predictedoutput that may be implemented by the disclosed methods and apparatusaccording to aspects of the present disclosure.

FIG. 7 shows a block diagram of an exemplary Siamese DCNN (SDCNN) thatmay be implemented with the disclosed apparatus according to aspects ofthe present disclosure.

FIG. 8 shows an example diagram of processing of data by one branch of aone dimensional (1D) SDCNN of FIG. 7 according to aspects of the presentdisclosure.

FIG. 9 illustrates a flow diagram of an exemplary method 900 for smallunmanned aerial systems (sUAS) detection and classification.

DETAILED DESCRIPTION

The embodiments of the present invention described herein are notintended to be exhaustive or to limit the invention to precise formsdisclosed. Rather, the embodiments selected for description have beenchosen to enable one skilled in the art to practice the invention.

The present disclosure provides systems and methods for detecting andclassifying a sUAS based on monitored uplink and downlink radiofrequency (RF) signals, radar and acoustic spectrograms based onmonitored radar and acoustical/sound data, and Electro-Optic/Infrared(EO-IR) images captured using EO/IR device such as cameras, as just afew examples. Detection and classification of an sUAS may furtherinclude the determination of hostile/non-hostile intent of the sUAS, theprediction of frequency hopping (FH) sequences of FH Spread Spectrum(FHSS) RC transmitters, and specific emitter identification (SEI) can beused to differentiate between multiple sUAS of the same type.

FIG. 1 shows an example of a multi-modal sUAS Detection andClassification system 100. It is noted here that for purposes of thisapplication, the term “multi-modal” is defined as detection andclassification using a plurality of modes such as RF detection, acousticdetection, electro-optic/infrared (EO/IR) detection, and radardetection, but the disclosure is not limited to such. The systems andmethods of the present invention addresses the problem of sUASdetection, classification, and reporting on flight mission intent fromsignatures captured from passive RF, Radar, Acoustic and EO/IR sensors,as illustrated in FIG. 1 . In particular, system 100 may include passiveRF sensors (not explicitly shown in FIG. 1 ) that capture RF spectra andgenerate RF spectrograms 102. Additionally, system 100 may includeelectro-optic and/or infrared sensors (not explicitly shown in FIG. 1 )that capture images/videos of objects such as sUASs in a vicinity of thesystem 100. The images/videos are shown representatively at 104.

Furthermore, the system 100 may include radar devices (not explicitlyshown in FIG. 1 ), such as Micro-Doppler devices that collect radar dataand generate radar spectrograms 106. Moreover, the system 100 mayinclude acoustical measuring/capturing/sensing devices (not explicitlyshown in FIG. 1 ) such as a microphone or microphone array for longerranges to collect acoustical data. This acoustical data may be used tothen generate acoustic spectrograms 108.

As further shown in FIG. 1 , the spectrograms 102, 106, 108 orimages/videos 104 may then be input to respective DCNN processors (toeliminate data redundancy and noise and extract progressively abstractfeatures for classification) 110 followed by a fusion process 112, suchas Softmax Score Fusion process as on example. This multi-sensorapproach, even with lack or obscuration of data from one or more of thesensor modalities, will still yield a correct decision resulting inmaximizing the probability of detection and minimizing the probabilityof false alarms. Object Detection Deep Convolutional Neural Networks(DCNNs) will be used to map from the detected signatures to sUAS classesalong with hostile/non-hostile decisions. The signatures include, butare not limited to, RF spectrograms, Micro-Doppler (i.e., due torotating rotors) and acoustic spectrograms computed from RF and Radarand Microphone sensors respectively, as well as video frames from EO/IRsensors.

FIG. 2 shows a specific example of a passive RF sensor system 200 thatmay be utilized in the system 100 shown in FIG. 1 , but the disclosureis not limited to such and various other implementations may becontemplated to achieve the described functionalities. For the passiveRF sensor 200 shown in FIG. 2 , an RF signal may be acquired by asoftware defined radio (SDR) such as a USRP X300/UBX-160 SDR 202 coupledto an antenna 204, such as a MARS MA-WO-UWB 138 MHz-6 GHz ultra widebandomnidirectional antenna followed by Mini Circuits Low Noise Amplifiersfor the 433 MHz, 915 MHz, 2.4 GHz and 5.8 GHz industrial, scientific andmedical (ISM) bands to detect RF signals between an radio control (RC)transceiver and sUAS controlled by the RC transceiver. In one example,the signal is sampled at a particular sample rate, such as at 100 MspsI/Q (In phase/Quadrature phase complex samples) for 1 second using a GNURadio SDR framework on a processor such as an Ubuntu 16.04 CyberPower PCequipped with a NVIDIA GTX 1070 Graphics Processing Unit (GPU) as shownat 206. The raw I/Q samples may be sequentially windowed and processedby a Fast Fourier Transform, such as a 16,384 point FFT, to generate ahigh resolution spectrogram for each 1 second file for offlineprocessing The spectrograms may also be resized to 256×256 pixels usinginterpolation, such as Nearest-Neighbor interpolation method for aGoogLeNet DCNN training/testing. This resizing is shown, in part, byinputs of the FFT spectrograms from SDR 202 and trained weights from theprocessor 206 to input 208, which is reduced by an “x” number of hiddenlayers shown at 2101 and 2102, as examples. The final output 212 outputsthe resultant potential sUASs that have been identified by the process.In this example, four different sUASs are shown to be identified (e.g.,3DR Aero, Parrot DISCO, DJ Phantom 4 Professional, and 3DR Iris+). Othertransmitters may include DJI Phantom 2 (P2), Phantom 3 Standard (P3S),Phantom 3 Advanced (P3A), Phantom 4 Standard (P4S), Phantom 4Professional+ (P4Pro+), Mavic Pro, Spark, Spektrum DX9, Spektrum DX7s,Devo-10 DSM2, 3DR Iris+Si4432, FrSky Taranis, FrSky DJT, FutabaFAAST14SG, Futaba S-FHSS handheld controllers and RMILEC T4070NB20,DragonLink, EzUHF, RangeLink and RFD900+Long Range System (LRS)transmitters and Fat Shark, Lumenier TX5G6R and TS58200 FPV (FirstPerson Video) transmitters.

In further aspects, a background class may be recorded for each of thebands (e.g., the four bands 433 MHz, 915 MHz, 2.4 GHz and 5.8 GHz).Variable signal to noise ratios (SNR) may also be simulated byattenuating the pixel values by a factor between 0.1 and 1.0. In oneexample, approximately 70% of the spectrograms may be used to train theDCNN and the remaining approximate 30% may be used for testing. In oneexample, training and testing may be accomplished using an NVIDIADIGITS/CAFFE Deep Learning (DL) framework, which is browser driven andallows to easily change DCNN hyperparameters and visualize the accuracyand loss curves during training. In one example, using such a frameworkmay result in classification accuracies of 100% for the 433 and 915 MHzbands, 99.8% for the 2.4 GHz band and 99.3% for the 5.8 GHz band.Similar high classification scores may be obtained on a datasetgenerated from limited look windows distributed over the one secondwaveform to simulate the scan and jam cycles of counter measure jammers(e.g., CREW jammers).

FIG. 3 shows one example of a spectrogram illustration 300 ofsimultaneous detection and classification of heterogeneous transmitterfrequency hops, Lightbridge bursts and background using a YOLOv2 ObjectDetection DCNN with the system of FIG. 1 . In this example, the YOLOv2Object Detection DCNN may be trained on 2048×2048 pixel sub spectrogramsgenerated from 4096 point FFTs performed over 4096 sequentialnon-overlapping frames from the beginning of a one second file and theirannotations (label files). The label files including bounding boxcoordinates for the RF objects in a spectrogram may be generated inKITTI (Karlsruhe Institute of Technology and Toyota TechnologicalInstitute) format and converted to the YOLO format using the open sourceAlps labeling tool (ALT). In this example, 120 spectrograms for each ofP3A, FASST14SG and DX9 transmitters were used including a backgroundclass annotated with 70% used for training and 30% for testing. Acomposite I/Q file was constructed by adding together unseen I/Q filesof the three RC transmitters and used for testing the trained ObjectDetection DCNN. FIG. 3 shows the detection and classification output ofthe DCNN. It may be seen in FIG. 3 that all RF objects have beendetected and classified (as indicated by the boxes and designations ofthe type of sUAS transmitter such as Spektrum DX9, Futaba FAAST14SG,etc.) which shows that simultaneous detection of heterogeneoustransmitters has been achieved.

FIG. 4 shows a block diagram 400 of an exemplary RFdetection/classification system that may implement the system describedin connection with FIG. 2 , for example. As illustrated the system 400includes a software defined radio (SDR) 402, which may be instantiatedwith a USRP X300/UBX-160 SDR as merely one example. The SDR 402 isfurther coupled with an antenna 404, which may be implemented with aMARS MA-WO-UWB 138 MHz-6 GHz ultra wideband omnidirectional antennafollowed by Mini Circuits Low Noise Amplifiers for the 433 MHz, 915 MHz,2.4 GHz and 5.8 GHz industrial, scientific and medical (ISM) bands todetect RF signals in one example. The SDR 402 is also coupled with aprocessing unit 406 (CPU or GPU). In an embodiment, the unit 406 may beimplemented with a NVIDIA GTX 1070 Graphics Processing Unit (GPU) or anNVIDIA Jetson TX2 (Maxwell GPU)/Jetson AGX Xavier (Volta GPU)/Jetson AGXOrin (Ampere GPU) embedded GPU platform. The SDR 402 and processing unit406 may be coupled with a high bandwidth connection such as by a 10Gigabit Ethernet. The processing unit 406 may be configured to implementone or more DCNN or common deep learning frameworks such as CAFFE,DIGITS, YOLO, PyTorch and TensorFlow.

In further aspects, it is noted that an object detection DCNN may alsobe applied to an EO simulation input in the form of RC flight simulatorvideo frames from a source such as a flight simulator (e.g., RealFlight7.5 simulator). Here, different types of aircraft may be “flown” in thisexperiment while capturing video of the flight simulator screen to aMPEG-4 file. In further aspects, a time period, such as 90 seconds of 30fps video, may be recorded for each aircraft with a number of frames pervideo extracted (e.g., 2,700 frames). The method also includes labelingby drawing a bounding box around the aircraft in each frame and labelingautomatically by running the frames through an ImageNet pre-trainedYOLOv2 model, as an example, which had aircraft as one of its categoriesfollowed by overwriting the “aircraft” label with the actual type ofaircraft being flown by modifying the YOLOv2 C code. The YOLOv2 objectDetection DCNN may be trained on the frames and annotations of allselected training aircraft. In further implementations of objectdetection, pixel coordinates of a detected aircraft and the confidencelevel are continuously displayed as the aircraft is tracked frame-byframe, and this procedure is applied to detect and classify sUAS in thepresent systems and methods. There are a number of quadcopter modelsthat may be input or are resident in flight simulators and videocaptured from such flight simulators can be used to initially train anEO Object Detection DCNN. Real-time video of quadcopters in flight maythen be further used for training the DCNN. The tracking of bounding boxpixel coordinates may be used to control the rotation of a video camera(Slew to Cue) mounted on a servo motor driven platform so that anaircraft is always in view at the center of the video frame. ObjectDetection DCNN may also be trained on the MS-COCO database to detectaircraft in the DIGITS/CAFFE DL framework. This alternative (backup)method can be used in inference mode on an NVIDIA Jetson TX2 embeddedGPU platform (e.g., 406 as shown in FIG. 4 for field deployment).Furthermore, TensorRT can be used on the Jetson TX2, for example, forreal-time inferencing.

FIG. 5 shows an exemplary frequency hop sequence for an RC controller.In this example, the particular frequency hop sequence was extractedfrom a DJI Mavic Pro RC handheld controller for a one second sample, butthose skilled in the art will appreciate that this is merely an exampleand the system will be applied to numerous and various controllers andtheir respective hop sequences. In a further development of the systemand method, RF object detection on the output of a real-time spectrumanalyzer (RTSA) may be utilized. In one example, a GNU Radio gr-fosphorutility can be used to display the RTSA waterfall (running spectrogram)on an X Window that is, in turn, output to a UDP port by FFmpeg (or anysimilar or equivalent multimedia conversion utility). In yet furtherexamples, YOLOv2 software may be employed to read streamed video framesfrom a User Datagram Protocol (UDP) port and perform real-time RF objectdetection and classification. A gr-fosphor block may also connect to aUHD (USRP Hardware Driver) USRP block in GNU Radio companion for liveacquisition of RF samples from the SDR (e.g., an X300/UBX-160 SDR).

For detection and classification of frequency hops, DJI Mavic Pro uplinkand background noise can be visualized. It is also noted that YOLOv2software (and updated versions up to YOLOv7) is open source C code thatuses the OpenCV and NVIDIA CUDA (Compute Unified Device Architecture)GPU libraries. Gr-fosphor uses the OpenCL library for parallel FFTcomputing and OpenGL for rendering the graphics, with both librariesrunning on the GPU (e.g., 406). The training set can be expanded toinclude all the different types of RC transmitters for sUASs referred toabove. In addition, a method for extracting the frequency hoppingsequences from FHSS RC transmitters may use the RF labeled output of theYOLOv2 software. A python script processes the sequential labeled subspectrogram frames (24 per 1 second I/Q recording) and outputtime-sorted hop sequences for a long short-term memory (LSTM) RecurrentNeural Network (RNN) may be used for training for frequency hopprediction.

FIG. 6 shows an example of a frequency hop sequence input to train anLSTM with a 1 second hop sequence (shown with first type of dashed/solidline 602) and a subsequent predicted hop sequence for the next 5 seconds(shown with second type of dashed/solid line 604) when no external inputis applied and the LSTM RNN evolves in time with its output fed back toits input. When hopping sequences of a new type of RC transmitter areneeded, this approach illustrated graphically in FIG. 6 can analyze theDCNN output and provide the prediction immediately after training.

In yet further aspect, another part of the system and method focuses onspecific emitter identification or RF finger printing. In order toaccomplish this finger printing, a Siamese DCNN (SDCNN) may be used forthis task to train on the nonlinearities (due to power amplifiers,manufacturing defects etc.) of radio transmitters according to someaspects as is illustrated in FIG. 7 . In particular, a Siamese DCNN istwo identical DCNNs (which share the same weights) through which a pairof input feature vectors from the same or different transmitters areapplied. In a still more particular embodiment, a one dimensional 1DSiamese DCNN with 4 convolutional layers may be utilized.

As shown in FIG. 7 , the SDCNN illustrated 700 includes a first DCNN 702receiving a first input 1 and a second DCNN 704 receiving a second input2. The SDCNN structure illustrated shows that DCNNs 702 and 704 sharethe same weights. The processed data (first and second weighted outputsor vectors G_(w)(X₁) and G_(W)(X₂)) is weighted and flows from the DCNNs702 and 704 to a distance function or module 706, which computes theEuclidean distance between the feature embeddings (vectors G_(w)(X₁) andG_(W)(X₂)), i.e. forward propagated inputs at the final layer. FIG. 7further shows the flow of data from the distance function 706 to thecomputation of a Hinge Embedding Loss (HEL) function or computationmodule 708, which is at the output of the DCNN. The HEL 708 measures thedegree of similarity of the pair of input vectors from the Euclideandistance between the feature embeddings, i.e. forward propagated inputsat the final layer.

Following the forward propagation, the gradient of the loss with respectto the DCNN parameters is computed and propagated backwards using a backpropagation algorithm to update the DCNN parameters in such a way as tomake the HEL small for inputs from the same transmitter and large forinputs from different transmitters. That is, the hinge embedding lossmodule 708 is configured to measure the degree of similarity of thefirst and second inputs from the Euclidean distance and determine agradient of the loss with respect to DCNN parameters of the SDCNN andensure that loss is small for same transmitters and large for differenttransmitters.

FIG. 8 shows an example 800 of one branch of the SDCNN illustrated inFIG. 7 . In this particular exemplary case, an SDCNN is trained onfeature vectors from preambles in ADSB (Automatic DependentSurveillance-Broadcast) message waveforms as a surrogate for sUAS RCtransmitter waveforms since the latter would require hundreds of RCtransmitters of the same model. ADSB messages are transmitted byaircraft at 1090 MHz to inform ground based receivers of their location.The ADSB transmission is sampled at 8 Mbps by a USRP software definedradio (SDR). The feature vector is the first difference of the phasecomputed from the Hilbert transform of the real component of 64 samplesspanning a preamble upsampled by 12.5. The ADSB dataset comprises 211unique Signal IDs (ICAO aircraft transmitter addresses). The test setconsists of waveforms from 14 Signal IDs that are a subset of trainingSignal IDs, but with different sets of waveforms. The total number ofpreambles in this example is 8,189 for the training set and 878 for thetest set. After training, the SDCNN of the present disclosure has showna similar/dissimilar classification decision accuracy of 95% on thetraining set and 75% on the test set. The code for the SDCNN wasdeveloped in PyTorch.

FIG. 9 illustrates a flow diagram of an exemplary method 900 for smallunmanned aerial systems (sUAS) detection and classification. Method 900includes monitoring RF frequencies in an environment in which at leastone sUAS is being operated and determine one or more RF spectrogramsbased on the monitored RF frequencies as shown at block 902. Further,method 900 includes capturing at least one of electro-optic informationor infrared (IR) information about the at least one sUAS as shown atblock 904.

Next, method 900 includes measuring radar information and determine oneor more radar spectrograms from the measured radar information for theenvironment in which at least one sUAS is being operated as shown atblock 906. In block 908, method 900 includes recording/capturingacoustical information in the environment in which at least one sUAS isbeing operated and generate one or more acoustic spectrograms based onthe recorded acoustical information. Finally, method 900 includesidentifying and/or classifying the at least one sUAS using at least onedeep convolutional neural network (DCNN) coupled with one or more of theRF sensor, the optical sensor, the radar sensor, and the sound sensor,the DCNN configured based on one or more of the one or more RFspectrograms, the electro-optic information or infrared (IR)information, the one or more radar spectrograms, and the one or moreacoustic spectrograms as shown in block 910.

Although the invention has been described in detail with reference tocertain preferred embodiments, variations and modifications exist withinthe spirit and scope of the invention as described and defined in thefollowing claims.

1. A small unmanned aerial systems (sUAS) detection and classificationsystem comprising: an RF sensor configured to monitor RF frequencies inan environment in which at least one sUAS is being operated anddetermine one or more RF spectrograms based on the monitored RFfrequencies; an optical sensor configured to capture at least one ofelectro-optic information or infrared (IR) information about the atleast one sUAS; a radar sensor configured to measure radar informationand determine one or more radar spectrograms from the measured radarinformation for the environment in which at least one sUAS is beingoperated; a sound sensor configured to record acoustical information inthe environment in which at least one sUAS is being operated andgenerate one or more acoustic spectrograms based on the recordedacoustical information; and at least one deep convolutional neuralnetwork (DCNN) coupled with one or more of the RF sensor, the opticalsensor, the radar sensor, and the sound sensor, the DCNN configured toidentify and/or classify the at least one sUAS based on one or more ofthe one or more RF spectrograms, the electro-optic information orinfrared (IR) information, the one or more radar spectrograms, and theone or more acoustic spectrograms.
 2. The sUAS detection andclassification system of claim 1, wherein the system is configured to:collect at least one frequency hop sequence of the RF signalcorresponding to at least one particular sUAS.
 3. The sUAS detection andclassification system of claim 2, wherein the system is configured to:train at least one long short-term memory (LSTM) recurrent neuralnetwork (RNN) based on the collected least one frequency hop sequence toenable prediction of subsequent transmission of the at least onefrequency hop sequence.
 4. The sUAS detection and classification systemof claim 1, further comprising: an ultra wideband (UWB) omnidirectionalantenna coupled to the RF sensor.
 5. The sUAS detection andclassification system of claim 1, further comprising: the at least onedeep convolutional neural network (DCNN) comprising a Siamese DCNN(SDCCN).
 6. The sUAS detection and classification system of claim 5,wherein the SDCNN comprises: a first DCNN configured to receive a firstinput and output a first weighted data; a second DCNN configured toreceive a second input and output a second weighted data, wherein thefirst and second DCNN share input weighting factors; a distance moduleconfigured to compute a Euclidean distance between the output first andsecond weighted data; and a hinge embedding loss (HEL) computationmodule configured to measure the degree of similarity of the first andsecond inputs from the Euclidean distance and determine a gradient ofthe loss with respect to DCNN parameters of the SDCNN
 7. The sUASdetection and classification system of claim 6, wherein the SDCNN isfurther configured to propagate the gradient of the loss backwards usinga back propagation algorithm to update the SDCNN parameters such thatthe HEL is a first value for inputs from a same RF transmitter andsecond value for inputs from different transmitters, wherein the firstvalue is less than the second value.
 8. A method for small unmannedaerial systems (sUAS) detection and classification comprising:monitoring RF frequencies in an environment in which at least one sUASis being operated and determine one or more RF spectrograms based on themonitored RF frequencies; capturing at least one of electro-opticinformation or infrared (IR) information about the at least one sUAS;measuring radar information and determine one or more radar spectrogramsfrom the measured radar information for the environment in which atleast one sUAS is being operated; recording acoustical information inthe environment in which at least one sUAS is being operated andgenerate one or more acoustic spectrograms based on the recordedacoustical information; and identifying and/or classifying the at leastone sUAS using at least one deep convolutional neural network (DCNN)coupled with one or more of the RF sensor, the optical sensor, the radarsensor, and the sound sensor, the DCNN configured based on one or moreof the one or more RF spectrograms, the electro-optic information orinfrared (IR) information, the one or more radar spectrograms, and theone or more acoustic spectrograms.
 9. The method for sUAS detection andclassification of claim 8, further comprising: collecting at least onefrequency hop sequence of the RF signal corresponding to at least oneparticular sUAS.
 10. The method for sUAS detection and classification ofclaim 9, training at least one LSTM RNN based on the collected least onefrequency hop sequence to enable prediction of subsequent transmissionof the at least one frequency hop sequence.
 11. The method for sUASdetection and classification of claim 8 further comprising: the at leastone deep convolutional neural network (DCNN) comprising a Siamese DCNN(SDCCN).
 12. The method for sUAS detection and classification of claim11, wherein the SDCNN comprises: a first DCNN configured to receive afirst input and output a first weighted data; a second DCNN configuredto receive a second input and output a second weighted data, wherein thefirst and second DCNN share input weighting factors; a distance moduleconfigured to compute a Euclidean distance between the output first andsecond weighted data; and a hinge embedding loss (HEL) computationmodule configured to measure the degree of similarity of the first andsecond inputs from the Euclidean distance and determine a gradient ofthe loss with respect to DCNN parameters of the SDCNN.
 13. The methodfor sUAS detection and classification of claim 12, wherein the SDCNN isfurther configured to propagate the gradient of the loss backwards usinga back propagation algorithm to update the SDCNN parameters such thatthe HEL is a first value for inputs from a same RF transmitter andsecond value for inputs from different transmitters, wherein the firstvalue is less than the second value.